Measurement of Hemoglobin A1c from Filter Papers for Population-Based Studies
Bibliographic record
Abstract
BACKGROUND: Stability and transport challenges make hemoglobin (Hb) A(1c) measurement from EDTA whole blood (WB) inconvenient and costly for large-scale population studies. This study investigated Hb A(1c) measurement from WB blotted on filter paper (FP) in a Level I National Glycohemoglobin Standardization Program (NGSP)-accredited laboratory. METHODS: Three Bio-Rad Variant™ II HPLC instruments and WB and FP specimens were used. Precision, accuracy, linearity, and readable total area of the 6.5-min (β-thalassemia method) Variant II HbA(2)/HbA(1c) Dual Program were assessed. Hb A(1c) stability was measured using in-house FP QC samples. The INTERHEART (a study of the effect of potentially modifiable risk factors associated with myocardial infarction in 52 countries) and CURE (Clopidogrel in Unstable Angina to Prevent Recurrent Events) studies provided chromatographs for morphometric analyses and interoperator variability experiments. Statistical analyses were performed to assess long-term sample stability, WB vs FP agreement, and significance of Hb A(1c) peak integration. RESULTS: Intra- and interassay CVs were ≤2.00%. Total area counts between 0.8 and 5.5 × 10(6) μV/s produced accurate Hb A(1c) results. The regression equation for agreement between WB(x) and FP(y) was as follows: y = 0.933x + 0.4 (n = 85). FP QC samples stored at -70 °C and tested over approximately 3 years yielded CVs of 1.72%-2.73% and regression equations with slopes of -1.08 × 10(-4) to 7.81 × 10(-4). The CURE study, with better preanalytical preparation, achieved a 97% reportable rate, and the reportable rate of the INTERHEART study was 85%. CONCLUSIONS: The FP collection method described provided accurate, robust, and reproducible measurement of Hb A(1c) using the Bio-Rad Variant II HPLC autoanalyzer when FP specimens were prepared according to standardized protocols, and analyses were performed in an NGSP-certified laboratory, supporting the use of FP collection cards in large multinational studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".